ActiveScope: Actively Seeking and Correcting Perception for MLLMs

Multimodal Large Language Models (MLLMs) have demonstrated impressive vision-language understanding, yet still struggle with fine-grained perception in high-resolution images. While existing training-free methods typically rely on attention-based localization or coarse-to-fine search, they are often misled by distractors and fail to locate multiple targets. Our investigation attributes these failures to *Contextual Dominance*, where salient distractors overwhelm target attention and cause inaccurate localization, and *Semantic Bias*, where global semantics cause the model to fixate on the most salient concept, resulting in incomplete localization in multi-object scenarios. Built on these insights, we propose ActiveScope, a training-free framework that enhances MLLMs by actively seeking and correcting perception. ActiveScope features two modules. The *Semantic Anchor Localization (SAL)* utilizes fine-grained semantic anchors to independently localize key targets, thereby mitigating semantic bias. The *Interference-Suppressed Refinement (ISR)* refines localization by suppressing attention on salient distractions to overcome contextual dominance. Extensive experiments on high-resolution image understanding benchmarks demonstrate that ActiveScope outperforms existing training-free methods (e.g., 96.34\% accuracy on $V^{*}$ Bench), validating the superiority of the active search and self-correction paradigm. Our code is available at https://github.com/jasmine-ww/ActiveScope.

Advanced AI models can understand both images and text well. However, they often struggle to find tiny details in large pictures. We noticed these models either get distracted by prominent objects or fixate on a single main subject. To solve this, we developed a new framework called ActiveScope. It acts like a smart magnifying glass for the AI. ActiveScope helps the system search for multiple objects and refine their exact locations. It achieves this by intentionally blocking out irrelevant background clutter to keep the AI focused. Our experiments show that this method improves the ability of AI to accurately perceive tiny details without requiring any costly retraining, which makes AI systems much more reliable for tasks that demand careful observation.